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IVIFCM-TOPSIS for bank credit risk assessment

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F19%3A39914914" target="_blank" >RIV/00216275:25410/19:39914914 - isvavai.cz</a>

  • Result on the web

    <a href="https://link.springer.com/chapter/10.1007/978-981-13-8311-3_9" target="_blank" >https://link.springer.com/chapter/10.1007/978-981-13-8311-3_9</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-981-13-8311-3_9" target="_blank" >10.1007/978-981-13-8311-3_9</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    IVIFCM-TOPSIS for bank credit risk assessment

  • Original language description

    Bank credit risk assessment is performed by credit rating agencies in order to reduce information asymmetry in financial markets. This costly process has been automated in earlier studies by using systems based on machine learning methods. However, such systems suffer from interpretability issues and do not utilize expert knowledge effectively. To overcome those problems, multi-criteria group decision-making (MCGDM) methods have recently been used to simulate the assessment process performed by the committee of multiple credit risk experts. However, standard MCGDM methods fail to consider high uncertainty inherently associated with the assessment and do not work effectively when the assessed credit risk criteria interact with each other. To address these issues, we propose MCGDM model for bank credit risk assessment that has two advantages: (1) The imprecise assessment criteria are represented by interval-valued intuitionistic fuzzy sets, and (2) the interactions among the criteria are modeled using fuzzy cognitive maps. When combined with traditional TOPSIS approach to ranking alternatives, we show that the proposed model can be effectively applied to assess bank credit risk.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/GA16-19590S" target="_blank" >GA16-19590S: Topic and sentiment analysis of multiple textual sources for corporate financial decision-making</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2019

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Intelligent Decision Technologies 2019 : Proceedings of the 11th KES International Conference on Intelligent Decision Technologies (KES-IDT 2019), Vol. 1

  • ISBN

    978-981-13-8310-6

  • ISSN

    2190-3018

  • e-ISSN

  • Number of pages

    10

  • Pages from-to

    99-108

  • Publisher name

    Springer Nature

  • Place of publication

    Heidelberg

  • Event location

    St. Julians

  • Event date

    Jun 17, 2019

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article